Full Paper

User-Specific Touch Models in a Cross-Device Context
Daniel Buschek, University of Munich (LMU), Germany
Simon Rogers, University of Glasgow, UK
Roderick Murray-Smith, University of Glasgow, UK
Time: Fri 10:03 - 10:27 | Session: Studies | Location: D 209

We present a machine learning approach to train user-specific offset models, which map actual to intended touch locations to improve accuracy. We propose a flexible framework to adapt and apply models trained on touch data from one device and user to others. This paper presents a study of the first published experimental data from multiple devices per user, and indicates that models not only improve accuracy between repeated sessions for the same user, but across devices and users, too. Device-specific models outperform unadapted user-specific models from different devices. However, with both user- and device-specific data, we demonstrate that our approach allows to combine this information to adapt models to the targeted device resulting in significant improvement. On average, adapted models improved accuracy by over 8%. We show that models can be obtained from a small number of touches (~60). We also apply models to predict input-styles and identify users.

MobileHCI 2013 Proceedings in the ACM Digital Library.

Important Dates

ACM Logo
LMU Logo


Google Logo
Grand Logo
Intel Software Logo
Microsoft Research Logo
Nokia Logo
SMI Logo
Telefonica Logo
Yahoo! Labs Logo